Metamaterials : design of an integrated high-impedance surface at 60 GHz, transposition and potentialities at 60 THz
Invisibility cloaking, sub-wavelength, thin antenna substrates, absorbers, etc., metamaterial structures have open many perspectives, some of them seeming futuristic while other being very practical given the current ste of the art in the domains of materials, microtechnologies and integrated optics.
this post-doctoral work will focus on the study of high-impedance surfaces and the possibility of transposition of these designs between very different frequency bands (6 GHz, 60 GHz, 60 THz) corresponding to a wide range of technologies and applications.
After a thorough bibliographic study of the current state of the art, the developments will include the design of high-impedance surfaces at the three frequency bands cited above and an experimental demonstration at 6 GHz and possibly at 60 GHz.
Global offshore wind turbines monitoring using low cost devices and simplified deployment methods
This project follows previous work focused on on-shore wind turbine instrumentation with inertial sensors networks whose dataflows allows the detection of vibration modes specific to the wind turbine components, in particular the mast and the real-time monitoring of these signals.
The objectives of this project are manyfolds: to bring this work to offshore wind turbines; search for signatures in wider frequency bands; study the behavior of offshore platforms and their anchorages.
One of the challenges is to find the signatures of rotating elements (blades) without direct instrumentation. Instrumentation of these elements is indeed more expensive and more impacting on the structure.
In addition, the sensor technology will be suitable for monitoring the fatigue life cycle of moving wire structures (dynamic electrical connection cable and anchoring) in the case of an off-shore wind turbine. The ultimate goal is to propose a global method for offshore wind turbine health monitoring.
Sizing and control optimisation of a hydrogen production system coupled with an offshore wind farm
Coupling MRE (Marine Renewable Energy) and hydrogen sectors reveal an important potential long-term assets. The MHyWind project suggests to estimate the energetic and economic potential of a hydrogen production system integrated into a substation of an offshore wind farm. The hydrogen produced and stored locally will be distributed by boat for harbour uses, as a replacement of fossil fuels. For that purpose, it will be organized a simulation which will integrate all the energy chain towards the harbour uses of hydrogen. It will allow to estimate various configurations and sizing according to the local uses, valuation leverages, control modes and behavior of the system. The criteria will be the producible (kg of H2 producted and used) and complet costs (CAPEX and OPEX). The objective of the postdoctoral student will be to develop the simulation tool on this applicative being fully integrated with the teams of concerned laboratories.
Numerical Meta-modelization based study of the propagation of ultrasonic waves in piping system with corroded area
The aim of the ANR project PYRAMID (http://www.agence-nationale-recherche.fr/Projet-ANR-17-CE08-0046) is to develop some technics of detection and quantification of the wall thinning due to flow accelerated corrosion in piping system. In the framework of this project involving French and Japanese laboratories, CEA LIST develops new numerical tools based on finite elements dedicated to the modelling of an ultrasonic guided wave diffracted by the corrosion in an elbow pipe. These solutions support the design of an inspection process based on electromagnetic-acoustic transduction (EMAT). To this end, the ability of CEA LIST to adapt meta-modeling tools of its physical models will be the key asset to allow intensive use of the simulation.
Post-doc: CNN neural network – managing data uncertainty in the learning database.
The aim is to develop algorithms able to take into account the uncertainty in the learning database of neural networks. The project fits into the context of the dynamic state estimation of liquid-liquid extraction and benefits of its knowledge-based simulator as well as industrial data. Indeed, the status of an industrial chemical process is accessible through operating parameters and available monitoring measures. However, the measures being inherently associated with uncertainty, it is necessary to make the data consistent with process knowledge. Therefore, the goal is to find the best data set of operational parameters (input of the knowledge-based simulator) to provide the model to estimate the real process state known through monitoring measures (output of the knowledge-based simulator). A convolutional neural network (CNN) is being developed in another postdoctoral project to solve the inverse problem to find the best input thanks to the measured output. A consistent set of operating parameters is going to be obtained and state of the process is going to be known during the dynamic regime of the liquid-liquid extraction process. This first step is to evaluate the impact of the uncertainty of operational parameters on the outputs of the knowledge-based model. This step will need to connect the knowledge-based model to URANIE, internal platform developed by CEA ISAS. This knowledge must be taken into account in the second part of the project. The uncertainty observed on the outputs should be taken into account in the learning loop to improve the estimation of the operational parameters by the CNN. The impact of these uncertainties on the CNN computed results must be assesed in order to trust the ability of the CNN to estimate the state of the process.
Through this project, we are at the heart of the thematic of digital simulation for the best control of complex systems.
Design of a safe and secure hypervisor in the context of a manycore architecture
The TSUNAMY project aims at thinking the design of future manycore chips in a collaborative hardware/software approach. It will investigate how crypto-processors can be incorporated into such a chip, turning it into a heterogeneous architecture, where scheduling, resource allocation, resource sharing, and resource isolation will be a concern.
The LaSTRE laboratory has designed Anaxagoros, a micro-kernel which ensures good properties in terms of safety and integration of mixed-criticality applications and is therefore well suited to the virtualization of operating systems. Making this virtualization software layer evolve in the context of the TSUNAMY project is the main goal of this post-doctoral proposal.
The first issue to address deals with the scalability of Anaxagoros on a manycore architecture. This system was designed with multicore scalability in mind : to help reach the highest level of parallelism in a lock-free fashion, innovative techniques were proposed to minimize the amount of synchronization points within the system. This is the first step, but scaling to manycore architectures brings new topics such as cache-coherency or non-uniform memory access that require to focus on data locality as well. The second challenge will be to incorporate genuine security features into Anaxagoros, e.g. regarding protection from covert channels, or confidentiality. The third and final challenge that will be addressed through interactions with the partners of the project is to devise techniques that could be implemented directly in hardware in order to ensure that even a breach in what is usually considered as trusted software will not allow an attacker to gain unprivileged data access or let information leak.
Optimal Multi Agent System management of smart heat grid using thermal storage
The aim of this work is a major contribution to a software framework based on coupling of Modelica/Jade environments that will allow to model, to simulate and to optimise the control of smart heat grid through dedicated thermal storage models development: interface specification to control the storages in the grid, simplified models design of heat grid’s most crucial components to be integrated in Agents (production, distribution/storage, consumption) and design of consumption and production forecast models in order to manage anticipation and improve the overall efficiency. The evaluation of performance is based on the test case build in Modelica simulation environment.
Fabrication and characterization of high thermal conductivity SiCf/SiC composites
SiCf/SiC ceramic matrix composites are foreseen candidates for structure materials and claddings in fast neutron reactor of 4th generation. However, their use may be limited because of their too low thermal conductivity in the operating conditions (< 10 W/mK).
SiCf/SiC ceramic matrix composites are now elaborated by chemical vapour infiltration (CVI). In order to improve their thermal conductivity (reduced porosity), it is planned to develop a hybrid elaboration process combining CVI and liquid routes.
The objective of this study is to determine the conditions of elaboration of a SiC matrix by liquid routes and then to characterize the thermo-mechanical behaviour of the hybrid composites, particularly in relation to CVI references.
Large-area processing and design of functional piezoelectric nanomaterials for flexible sensors and systems
CEA LETI develops innovative highly flexible strain sensors which exploit the piezoelectric properties of self-organized gallium nitride nanowires. The fabrication steps are basically: i) nanowire growth, ii) nanowire assembly, iii) encapsulation, iv) contacting. First demonstrators with small active area (1.5 cm²) have already been achieved using the Langmuir Blodgett (LB) technique for the assembly of nanowires. The present project is concerned with the scaling-up of the assembly process over large surface areas, as well as controlled patterning of nanowire assemblies in 1D and 2D by using an innovative CEA LITEN roll-to-roll technology called Boostream® which has the same functionalities as LB in its basic function.
The aim of the post doc is to develop a new building block for the Boostream® equipment enabling a controlled assembly of wires with a pre-defined design. The candidate will carry out studies to optimize the wire assembly, develop the process of film patterning and fabricate, integrate and characterize GaN nanowire piezoelectric transducers with dimensions of 15x15 cm².
More generally, this post doc will also provide the opportunity to develop a generic knowledge to manipulate micro or nano wires or fibers giving new solutions in various fields such as surface structuration, electronic skin, energy...
Internet of Things applications: Ultra Low power and adaptive analog-to-digital converters in advanced FD-SOI process
The post-doctoral project aims to study Ultra Low Power and Adaptive Analog-to-Digital Converter (ADC) over a wide operating range of microsystem from Internet of Things or sensor networks applications.
The ADC is one of the main blocks into System on Chip (SoC) because of its position between physical signal treatment (Front-End) and digital treatment (Digital Base Band). Its performances in terms of resolution or frequency ranges affect the overall performances of the SoC. A particular consideration will be carrying out on power consumption and some reconfigurability technics will be used to adapt its consumption to the contextual performances required. To reduce as possible the ADC consumption, advanced FDSOI process will be used.
Based on Ultra Low Power constraints, the post-doctorate student will study the literature and will propose, design and experimentally demonstrate a relevant topology to increase the power efficiency and the performances of ADC by using advanced FDSOI process.